Hire AI/ML developers who understand enterprise delivery, not only prototypes.

What this developer/team can own

Embedded in days · Remote or on-site · WebSenor Group Hire AI ML Engineers Senior AI/ML engineers who build and ship production-grade LLM apps, RAG pipelines, computer vision systems and predictive models — not research decks. 13+ years delivering enterprise software, senior-only, engaged in days. Hire an AI/ML Engineer Talk to Us 13+ Years delivering 60+ AI/ML engagements 45+ AI/ML engineers 4.8★ Clutch First model shipped in week 1 AS FEATURED IN ForbesYourStoryInc42CLUTCH ★GoodFirmsThe HinduEconomic Times ● WHAT AI/ML ENGINEERS DELIVER Engineers who ship models, not slide decks. When you hire AI ML engineers from WebSenor, you get production code in your stack — not a proof-of-concept notebook that never leaves a laptop. ★ Most requested 🤖 LLM & Generative AI Development Custom applications built on GPT, Claude, Gemini and open-source LLMs — chat interfaces, copilots, content generation and structured extraction. 📚 RAG Pipelines & Vector Search Retrieval-augmented generation built on your own documents and data — embeddings, chunking strategy, vector databases and re-ranking done right. ★ Most requested 🧠 Custom ML Model Development Classification, regression and ranking models trained on your data — from feature engineering through evaluation and production hand-off. 🕹️ AI Agent & Workflow Automation Multi-step agents that call tools, query databases and take action inside your existing systems — built with LangChain, LlamaIndex or custom orchestration. ⚙️ MLOps & Model Deployment Training pipelines, model versioning, monitoring and CI/CD so models keep working after launch, not just in the demo. 👁️ Computer Vision Solutions Object detection, OCR, image classification and quality-inspection models deployed on cloud or edge hardware. ★ Founder favourite 💬 NLP & Text Analytics Sentiment analysis, entity extraction, document classification and summarization pipelines for support, legal and content teams. 📈 Predictive Analytics & Forecasting Demand forecasting, churn prediction and anomaly detection models tied directly into dashboards your team already uses. 🗄️ Data Engineering & Pipeline Development Clean, reliable ETL and feature pipelines — because every AI/ML engagement lives or dies on the data feeding it. Need to hire AI ML engineers this week?Free 30-min scoping · NDA day 1 · Reply in 4 hours. Get matched → ● THE ROLE Why companies hire AI ML engineers instead of a general dev team. AI/ML engineering sits between data science and production software — it needs someone who understands model behaviour, evaluation and cost, but who can also ship clean, maintainable code into a real system. A generalist developer without ML depth tends to over-promise on accuracy and under-plan for drift, latency and inference cost. Our AI/ML engineers close that gap: they've shipped LLM and ML features into live products, not just Kaggle notebooks. ✓ Ships production-ready models — with evaluation, monitoring and rollback built in ✓ Fluent in the modern AI stack — LLM APIs, vector databases, agent frameworks, classic ML ✓ Senior by default — no hand-holding, no ramp-up theatre ✓ Honest about limits — will tell you when a use case doesn't need AI at all Our AI/ML engineers draw from the same senior bench behind our Forward Deployed Engineers and dedicated engineering teams — see real engagements in our case studies. 60+ AI/ML engagements <2 wks Avg. time to first prototype 4.8★ Clutch rating 90% 12-month retention ● WHO WE BUILD FOR Industries where our AI/ML engineers deploy. 🏥 HealthcareDiagnostic imaging & triage models 🏦 FintechFraud detection & risk scoring 🛍️ E-commerce & RetailRecommendations & search ranking 🏭 ManufacturingVision-based quality inspection 🚚 LogisticsDemand & route forecasting 🎓 EdTechAdaptive learning & grading AI 🏢 Real EstateValuation & lead-scoring models ⚖️ Legal TechDocument review & contract NLP ● SELECTED WORK AI/ML engagements that shipped. A sample of recent work — see the full write-ups in our case studies. HEALTHTECH Diagnostic imaging model, deployed in 5 weeks Computer vision model embedded into hospital triage workflow. FINTECH Fraud detection ML pipeline Real-time scoring model cut false positives by 38%. E-COMMERCE Personalization recommendation engine Lifted average order value by 18% within one quarter. SAAS RAG-powered support agent, deployed in-Slack Deflects 65% of the client's tier-1 tickets. LOGISTICS Demand forecasting model Reduced stock-outs across an 8-warehouse network. LEGAL TECH Contract review NLP tool Cut manual contract review time by 70%. Not sure your idea needs AI/ML?Tell us the use case — we'll tell you honestly if it needs a model at all. Get a free assessment → ● PROCESS From brief to first shipped model in under two weeks. 01 Discovery call30 minutes on your data, use case, and whether AI/ML is actually the right tool for it. 02 Engineer match2–3 vetted profiles matched to your data, stack and domain, usually within 4 hours. 03 Technical trialA short paid trial task against your real data — see the model take shape before you commit. 04 Data & environment onboardingSecure access to data sources, repos and cloud environment — under NDA from day one. 05 First model or prototype shippedA working baseline in your environment within the first two weeks — measured, not just demoed. 06 Ongoing tuning & supportMonitoring, retraining and scope changes as usage grows, with a 30-day cancellation window — no lock-in. ● HOW IT'S DIFFERENT AI/ML Engineer vs. Data Scientist vs. Software Engineer. Three roles that get confused constantly — here's the actual line between them. AI/ML Engineer ✓ Builds and ships production model code ✓ Owns the full pipeline — data to deployment ✓ Handles monitoring, drift and inference cost ✓ Judged on what's running in production Data Scientist • Focuses on analysis & experimentation • Answers "what does the data show" • Rarely owns production deployment • Judged on insights & model accuracy Software Engineer • Builds application logic & infrastructure • Treats models as an external API call • Not typically trained on model behaviour • Judged on system reliability ● SKILLS COVERED What our AI/ML engineers bring. Technical Python & PyTorch / TensorFlowLLM APIs (OpenAI, Anthropic, Vertex AI)LangChain / LlamaIndexVector Databases (Pinecone, Weaviate, pgvector)Scikit-learn & XGBoostComputer Vision (OpenCV, YOLO)MLOps (MLflow, Kubeflow, SageMaker)AWS / GCP / Azure MLDocker & KubernetesSQL & Data Pipelines Client-Facing Use-Case DiscoveryModel Evaluation ReportingStakeholder CommunicationAgile & Sprint PlanningDocumentation & Model CardsCost & Latency Trade-off AdvisingCross-functional CollaborationResponsible AI & Bias ReviewScope & Time ManagementSuccess Metrics Definition Transparent pricing What it costs to hire AI ML engineers. Hourly or dedicated monthly — no retainers, no account-manager overhead. Pay for hours used, cancel with 30 days' notice, no long-term lock-in. Part-time engagement $28 – $40/hr 10–20 hrs/week, ideal for a single model or LLM feature. Full-time dedicated engineer $5,000 – $8,500/mo Fully dedicated, one engineer, one AI/ML roadmap. AI/ML pod (2–4 engineers) Custom quote For multi-model programs spanning data, ML and MLOps. Get a fixed quote — within 4 hours Free scoping call · Paid trial task · NDA day 1 ● FAQ Hire AI ML Engineers — common questions. Why hire AI ML engineers from WebSenor instead of a generalist agency? When you hire AI ML engineers from WebSenor, you get senior specialists who've shipped LLM, RAG and classic ML systems into live products — not generalist developers experimenting with a new framework on your budget. How is an AI/ML Engineer different from a Data Scientist? Data Scientists focus on analysis and experimentation; an AI/ML Engineer owns the full path from data to a running production system, including deployment, monitoring and cost. How fast can WebSenor deploy an AI/ML engineer? You'll get 2–3 vetted profiles within 4 hours of a discovery call. Most engagements go from first call to a working prototype within two weeks. What does it cost to hire AI ML engineers? Part-time engagements start at $28–40/hr; full-time dedicated engineers run $5,000–8,500/month. No retainers — you pay for hours used, with a 30-day cancellation window. Can I hire an AI/ML engineer part-time or month-to-month? Yes — flexible month-to-month engagements with no long-term lock-in. Most clients stay 6–12+ months once the model is in production, but there's no contract requiring it. What AI/ML stacks do your engineers cover? Python, PyTorch/TensorFlow, LLM APIs (OpenAI, Anthropic, Vertex AI), LangChain/LlamaIndex, vector databases, classic ML with Scikit-learn/XGBoost, computer vision, and MLOps tooling on AWS/GCP/Azure. Do AI/ML engineers work on-site or remote? Both. Most engagements run remote — full Slack/repo/data access and daily overlap with your team — with on-site work available for engagements that need it. What happens if the engineer isn't a good fit? A 90-day replacement guarantee applies for dedicated hires, and contract engagements can be swapped or cancelled with 30 days' notice — no extra charges. Do I need my own labeled data to start? Not always. For LLM/RAG use cases, existing documents are often enough. For custom classification or forecasting models, our engineers will assess your data during the discovery call and flag any gaps before work begins. Do your AI/ML engineers help with LLM and generative AI projects specifically? Yes — LLM application development, RAG pipelines, and AI agent workflows are core to our AI/ML engineering practice, not a separate add-on service. Related Roles Forward Deployed EngineersCustomer-embedded buildersBackend EngineersNode · Python · JavaDevOps / SRE EngineersCI/CD · ObservabilityAll Hire Stacks →Browse every role Ready to hire AI ML engineers for your next build? 30-min discovery call with a senior AI/ML engineer. Honest feasibility, realistic timeline, transparent estimate within 24 hours. Hire an AI/ML Engineer Talk to Us

• RAG applications
• AI agents and workflow automation
• Predictive analytics
• Computer vision features
• Enterprise AI integrations
• Model evaluation and monitoring